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Impact Of Web (URL) Phishing and Its Detection

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  • Kunle Oloyede,
  • Chinenye Obunadike,
  • Simo Yufenyuy,
  • Emmanuel Elom,
  • Abdul-Waliyyu Bello,
  • Somtobe Olisah,
  • Callistus Obunadike,
  • Oluwadamilola Ogunleye,
  • Sulaimon Adeniji
Kunle Oloyede
Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA
Chinenye Obunadike
Anambra State University Uli, Anambra State
Simo Yufenyuy
Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA
Emmanuel Elom
Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA
Abdul-Waliyyu Bello
Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA
Somtobe Olisah
Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA
Callistus Obunadike
Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA
Oluwadamilola Ogunleye
George Washington University, Washington DC, USA
Sulaimon Adeniji
University of Lagos, Lagos State
Published in : International Journal of Scientific Research and Management (IJSRM) , Vol. 12 No. 04 (2024)

Submission to VIJ 2024-04-13

Keywords

  • Web phishing, machine learning, real time URL analysis detection techniques

Copyright (c) 2024 Kunle Oloyede, Chinenye Obunadike, Simo Yufenyuy, Emmanuel Elom, Abdul-Waliyyu Bello, Somtobe Olisah, Callistus Obunadike, Oluwadamilola Ogunleye, Sulaimon Adeniji

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Abstract: Web phishing is a persuasive and evolving cyber threat that poses significant risks to individuals, businesses, and organizations in the modern digital age. This paper aims to provide an overview of web phishing, focusing on its methods, detection techniques, and prevention. Phishing attacks occur when malicious actors use deceptive practices to trick people into divulging sensitive or classified information such as passwords, credit card details, or personal data. These attacks primarily manifest through emails, websites, or social engineering tactics. Phishing emails often impersonate trusted entities and lure recipients into clicking on malicious links or downloading harmful attachments. Web phishing involves using fraudulent websites that mimic legitimate ones to steal user information or deliver malware. Detecting web phishing attacks is an ongoing challenge due to the sophistication of attackers. Several detection techniques have been developed, including heuristic analysis, machine learning algorithms, and real-time URL analysis. These methods analyze various attributes of websites and emails to identify suspicious patterns or behaviors. Detecting web phishing is equally essential. Effective prevention strategies include user education and awareness programs, using two-factor authentication, regular software updates, and deploying advanced email filtering and anti-phishing tools. User training is crucial in helping individuals recognize phishing attempts and avoid falling victim to them.

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